Читать книгу Minding the Machines - Jeremy Adamson - Страница 17

Understanding the first-mover advantage associated with data and analytics, many organizations of means yet with limited analytical maturity have hired dozens of practitioners to rapidly scale up and advance their data capabilities. After 18 to 24 months, these large technocratic organizations review the cost/benefit of the new $10M division and question the value of analytics to the enterprise. Moving too quickly, without the cultural or technical infrastructure to support it, can put an end to analytical ambitions before the first hire has been made.

Alternatively, many organizations have moved too cautiously and hired one or two early-career employees and placed them under a line manager with conflicting priorities and limited analytical understanding. The maturity of the data science team in this case often peaks with gentle scripting and automation, and executives are again left questioning the value of analytics.

It can seem that a Goldilocks approach of creating a mid-sized team could work, but the issue in both extremes has not been the number of people involved, it has been with a lack of a defined strategy. To prevent this situation, organizations need to have a clear objective in mind for the team, an understanding of what the field of advanced analytics and AI is, and the support that is required to make it a success. Further, organizations cannot look within to build out this strategy, as they thoroughly lack the capabilities to define this strategy. It is only by consultation with others and thorough benchmarking to industry and practice standards that this strategy can be developed.